The current study performed in order to detect and quantify epicatechin in two tea samples of Camellia sinensis (black and green tea) by thin layer chromatography (TLC) and high performance liquid chromatography (HPLC). Extraction of epicatechin from black and green tea was done by using two different methods: maceration (cold extraction method) and decoction (hot extraction method). Qualitative and quantitative determinations of epicatechin in two tea samples were investigated. Epicatechin identification was made by utilizing preliminary chemical tests and TLC. This identification was also boosted by HPLC and then quantified epicatechin in all ethyl acetate fractions of two tea samples. This research revealed the existence of epicatechin in black and green tea according to TLC and HPLC. The 50% aqueous ethanol was better solvent for extraction of epicatechin from leaves of tea. Quantitative estimation of epicatechin by HPLC revealed that ethyl acetate fraction of DGTAE contains the higher concentration of epicatechin than other analyzed fractions. Conclusion, tea is an excellent source of catechins particularly epicatechin that possessed various pharmacological effects.
Out of 150 clinical samples, 50 isolates of Klebsiella pneumoniae were identified according to morphological and biochemical properties. These isolates were collected from different clinical samples, including 15 (30%) urine, 12 (24%) blood, 9 (18%) sputum, 9 (18%) wound, and 5 (10%) burn. The minimum inhibitory concentrations (MICs) assay revealed that 25 (50%) of isolates were resistant to gentamicin (≥16µg/ml), 22 (44%) of isolates were resistant to amikacin (≥64 µg/ml), 21 (42%) of isolates were resistant to ertapenem (≥8 µg/ml), 18 (36%) of isolates were resistant to imipenem (4- ≥16µg/ml), 43 (86%) of isolates were resistant to ceftriaxone (4- ≥64 µg/ml), 42 (84%) of isolates were resistant to ceftazidime (1
... Show MoreCommunity detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algo
... Show MoreWe aimed to examine the potential protective effects of Iraqi
Rats were assigned to four groups, six in each group. Group I: rats were administered a daily oral dose of 1 mL/kg/day of distilled water. Group II: rats were intraperitoneally injected with 70 mg/kg DEN once per week for 10 conse
This work presents a comparison between the Convolutional Encoding CE, Parallel Turbo code and Low density Parity Check (LDPC) coding schemes with a MultiUser Single Output MUSO Multi-Carrier Code Division Multiple Access (MC-CDMA) system over multipath fading channels. The decoding technique used in the simulation was iterative decoding since it gives maximum efficiency at higher iterations. Modulation schemes used is Quadrature Amplitude Modulation QAM. An 8 pilot carrier were
used to compensate channel effect with Least Square Estimation method. The channel model used is Long Term Evolution (LTE) channel with Technical Specification TS 25.101v2.10 and 5 MHz bandwidth bandwidth including the channels of indoor to outdoor/ pedestrian
Background: Change in palatal vault shape and Reinforcement of high impact acrylic denture base resin may in turn affect the dimensional accuracy of acrylic resin and affecting the fitness of the denture.This study evaluated tostudy the effect of fiber reinforcement for high-impact acrylic resin denture base with different palatal vault shapes on adaptation or gap space between the denture base and the stone cast and compare with non-fiber reinforcement and effect of palatal vault shapes on adaptation of non-reinforced and fiber reinforced high impact denture base acrylic resin Material and method: Three different palatal vault shapes were prepared on standard casts using CNC (computer numerical control) machine. 60 samples of heat polymeri
... Show MoreBackground: Change in palatal vault shape and Reinforcement of high impact acrylic denture base resin may in turn affect the dimensional accuracy of acrylic resin and affecting the fitness of the denture. The aim of study is to evaluate the effect of fiber reinforcement for high-impact acrylic resin denture base with different palatal vault shapes on linear dimensional change and effect of palatal vault shapes on linear dimensional changes of non-reinforced and fiber reinforced high impact denture base acrylic resin Material and method: Three different palatal vault shapes were prepared on standard casts using CNC (computer numerical control) machine. 60 samples of heat polymerized high impact acrylic resin maxillary denture base were fabri
... Show MoreBackground: Debonding and fracture of artificial teeth from denture bases are common clinical problem, bonding of artificial teeth to heat cure acrylic and high impact heat cure acrylic denture base materials with autoclave processing method is not well known. The aim of this study was to evaluate the effect of autoclave processing method on shear bond of artificial teeth to heat cure denture base material and high impact heat cure denture base material. Materials and methods: Heat polymerized (Vertex) and high impact acrylic (Vertex) acrylic resins were used. Teeth were processed to each of the denture base materials after the application of different surface treatments. The sample (which consist of artificial tooth attached to the dentur
... Show MoreAutism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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